Gangapuram, Harshini

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    A Bayesian machine learning approach for EEG functional connectivity estimation and working memory load classification in human subjects
    (2024-05) Gangapuram, Harshini; Manian, Vidya; College of Engineering; Vega, José Fernando; Juan, Eduardo J.; Meléndez, José; Department of Electrical and Computer Engineering; Cruzado Vélez, Ivette
    Analyzing working memory is essential for understanding cognitive processes and improving educational strategies, mental health diagnostics, and psychological interventions. Electroencephalogram (EEG) signals, known for their high temporal correlation, effectively capture these subtle responses, highlighting the importance of assessing EEG-based functional connectivity across various frequency bands to understand brain dynamics under varying cognitive loads. Traditional methods, typically involving regression models, often face challenges like biased connectivity estimates due to enforced sparsity and inaccuracies from small sample sizes or sampling noise. Addressing these issues, the current study develops a Bayesian structure learning algorithm to learn the functional connectivity of EEG. This approach ensures accurate connectivity analyses across different frequency bands. Next, functional connectivity features are given as an input to graph convolutional network to classify working memory loads. This study analyzes five working memory datasets to evaluate the proposed methodology. The subject-specific classification yields an average sensitivity and specificity of 92% and 94%, respectively. The proposed methodology produced consistent results in functional connectivity estimation compared to state-of-the-art functional connectivity metrics. The study finds that encoding information is critical in altering functional connectivity for different working memory loads rather than its manipulation/retention of tasks.